Cyclostationary modeling of ground reaction force signals

Sabri, Khalid; Badaoui, Mohamed El; Guillet, François; Belli, Alain; Millet, Guillaume Y.; Morin, Jean–Benoît

doi:10.1016/j.sigpro.2009.09.027

Cited by 30 publications

(11 citation statements)

References 13 publications

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“…Recent applications are in the following subjects: analysis of genome signals [19], neuroscience [20], ballistocardiogram analysis [128], heart and lung sound separation [129], analysis of the embolic blood Doppler signal [132], foetal PQRST extraction from electrocardiogram (ECG) recordings [143], heart and respiration rates monitoring [183], heart sound signal selection [217], heart sound cancelation from lung sound [218], detection and characterization of a runner's fatigue [38,235], analysis of electromyographic signals [296], modeling and analysis of ground reaction force signals [97,300], and analysis of myoelectric signals [3].…”

Section: Biological Signalsmentioning

confidence: 99%

Cyclostationarity: New trends and applications

Napolitano

2016

Signal Processing

184

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Section: Biological Signalsmentioning

confidence: 99%

Cyclostationarity: New trends and applications

Napolitano

2016

Signal Processing

184

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“…Cyclostationarity process has been used earlier in the field of telecommunications by "Gardner" [8], then was extended to the field of mechanics by "Antoni et al" [9], and recently used in the field of biomechanics by "Piscione et al" [10]; "Cao et al" [11]; "Sabri et al" [12] and "El Badaoui & Bonnardot" [13]. CS is a property of non-stationarity.…”

Section: Cyclostationarity Character Of Walking Signalsmentioning

confidence: 99%

Contribution of the cyclic correlation in gait analysis: Variation between fallers and non-fallers

Zakaria

Toulouse

Badaoui

et al. 2014

2014 IEEE 16th International Conference on E-Health Networking, Applications and Services (Healthcom)

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there have been numerous studies involving research and development, for detecting falls exhibited by the elderly. Considering that the prevention of a falling elderly is much more complex to address and estimate, very little research has been done. In fact research is often strictly limited resourceful medical organizations that have specialized clinical tools. Human locomotion, particularly "Walking" is defined by sequences of cyclic and repeated gestures. The variability of such sequences can reveal information about drive failure and motor / motor-neuron disorders. Studying and exploiting the Cyclostationary (CS) properties of such sequences, offers a complementary way to quantify human locomotion and its changes with progressing aging and the development of diseases. This quantization may provide an insight into the neural function and the neural control of walking which would be altered by changes associated with aging and the presence of certain diseases. As part of the collaboration between LASPI and CHU Saint Etienne, we decided to focus on certain advanced signal processing theory and methods, to study very complex phenomena of human walking, which is often subject to numerous motor and / or motor-neurons malfunctions, such as in the case of the falling elderly population, that often has serious and severe consequences. Furthermore, this paper also examined the effects on walking in elderly subjects in three task conditions: (a) single task (MS) and (b) dual task: walking by performing a fluency task(MF) and (c) walking while backward counting (MD). Results show that the conditions of walking impacted the Cyclostationarity and its known indicator: the cyclic autocorrelation function. Such indicator also evolved between fallers and non-fallers and between the fallers who have history of falls and those who haven't.

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“…The repetition is not exactly periodic but is considered quasi-periodic. It means that the vibration wave recordings are cyclostationary [1,13,16]. Many natural or man-made processes in various fields arise from periodic phenomena, as mechanics, telecommunications, radio astronomy, econometric, … These processes, although not periodic functions of time, give rise to random data whose statistics vary periodically with time and are called cyclostationary processes [20].…”

Section: Introductionmentioning

confidence: 99%

Cyclic Analysis of Phonocardiogram Signals

Choklati¹,

Sabri²,

Lahlimi³

2017

IJIGSP

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Abstract-Acoustic vibrations of the heart in time domain correspond to phonocardiogram (PCG) signal. A PCG signal, in the healthy case, consists of two fundamental sounds s 1 and s 2 produced by the mechanical functioning of the heart. Abnormalities in the heart valves correspond to other cardiac sounds than s 1 and s 2 . This makes PCG signal a valuable tool related to the track of heart diseases. Actually, the characterization and the analysis of PCG signals is being a fertile area of study and investigation. However, most of the topics which treated this area of research focused only on timefrequency analysis, without exploiting the periodic character of PCG signal due to the limitations of the PCG modeling. In this work, we propose a coherent mathematical model for PCG signals based on cyclostationarity and Gabor kernel. The motivation behind is to define a framework, utilizing cyclic statistic due to noise robustness, for a full description of PCG signals, which leads to an easy and efficient early identification of certain heart abnormalities. The validation of the proposed model and its capacity to reflect the heart functioning is tested over synthetic and real data sets.

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Cyclostationary modeling of ground reaction force signals

Cited by 30 publications

References 13 publications

Cyclostationarity: New trends and applications

Cyclostationarity: New trends and applications

Contribution of the cyclic correlation in gait analysis: Variation between fallers and non-fallers

Cyclic Analysis of Phonocardiogram Signals

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